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        <blockquote>
<p>记得大一的时候要装Tensorflow，为了避免麻烦就直接用按网上的教程用Anaconda装了，当时也不太用python，就没注意Anaconda到底能干什么，最近重新学习python，发现是个库Anaconda都已经有了，才知道这玩意这么全能。所以打算写个文章记下Anaconda到底内含了哪些库。</p>
</blockquote>
<h2 id="what-is-anaconda"><a class="markdownIt-Anchor" href="#what-is-anaconda"></a> What is Anaconda ?</h2>
<p>Anaconda是一个免费开源的Python和R语言的发行版本，用于计算科学（数据科学、机器学习、大数据处理和预测分析），Anaconda致力于简化包管理和部署。Anaconda的包使用软件包管理系统Conda进行管理。超过1200万人使用Anaconda发行版本，并且Anaconda拥有超过1400个适用于Windows、Linux和MacOS的数据科学软件包。</p>
<h2 id="anaconda内置的那些库"><a class="markdownIt-Anchor" href="#anaconda内置的那些库"></a> Anaconda内置的那些库</h2>
<h3 id="jupyter系"><a class="markdownIt-Anchor" href="#jupyter系"></a> jupyter系</h3>
<p>Jupyter是一个非营利组织，旨在“为数十种编程语言的交互式计算开发开源软件，开放标准和服务”。2014年由Fernando Pérez从IPython中衍生出来，Jupyter支持几十种语言的执行环境<br />
<img src="https://s2.ax1x.com/2020/02/10/14Nw40.png" alt="" /><br />
jupyter系的第一件产品时Ipython，IPython是一种基于Python的交互式解释器。相较于本地的Python Shell，IPython提供了更为强大的编辑和交互功能。后来在这个基础之上延伸出了<strong>jupyter notebook</strong>。</p>
<p>Jupyter Notebook是一个基于Web的交互式计算环境，用于创建Jupyter Notebook文档。Notebook一词可以通俗地引用许多不同的实体，主要是Jupyter Web应用程序、Jupyter Python Web服务器或Jupyter文档格式。Jupyter Notebook文档是一个JSON文档，遵循版本化模式，包含一个有序的输入/输出单元格列表，这些单元格可以包含代码、文本、数学、图表和富媒体，通常以“.ipynb”结尾扩展。</p>
<p>jupyter notebook最神奇的地方在于单元格支持markdown语法、html语言和Latex公式编辑，你可以上面写上Latex公式下面就开始用它进行实验<br />
<img src="https://s2.ax1x.com/2020/02/10/14NWU1.png" alt="" /></p>
<p>2018年jupyter notebook的下一代产品<strong>jupyter lab</strong>发布了。它在一个灵活且强大的用户界面中提供了经典的Jupyter Notebook（笔记本、终端、文本编辑器、文件浏览器、丰富输出等）所有熟悉的构建模块。<br />
<img src="https://s2.ax1x.com/2020/02/10/14NovD.png" alt="" /><br />
Lab相比较Notebook最大的优势在于它的用户界面集成强，适合多文档协助工作。</p>
<p>JupyterLab有以下特点：</p>
<ul>
<li>
<p>交互模式：Python交互式模式可以直接输入代码，然后执行，并立刻得到结果，因此Python交互模式主要是为了调试Python代码用的</p>
</li>
<li>
<p>内核支持的文档：使你可以在Jupyter内核中运行的任何文本文件（Markdown，Python，R等）中启用代码</p>
</li>
<li>
<p>模块化界面：在同一个窗口同时打开好几个notebook或文件（HTML, TXT, Markdown等等），以标签的形式展示，更像是一个IDE</p>
</li>
<li>
<p>镜像notebook输出：让你可以轻易地创建仪表板</p>
</li>
<li>
<p>同一文档多视图：使你能够实时同步编辑文档并查看结果</p>
</li>
<li>
<p>支持多种数据格式：查看并处理多种数据格式，也能进行丰富的可视化输出或者Markdown形式输出</p>
</li>
<li>
<p>云服务：使用Jupyter Lab连接Google Drive等服务，极大地提升生产力</p>
</li>
</ul>
<p>看了一下介绍，觉得jupyter Lab简直好用到爆，不知道github对它的支持咋样。</p>
<h3 id="spyder"><a class="markdownIt-Anchor" href="#spyder"></a> spyder</h3>
<p>Spyder是Python(x,y)的作者为它开发的一个简单的集成开发环境。和其他的Python开发环境相比，它最大的优点就是<strong>模仿MATLAB的“工作空间”的功能</strong>，可以很方便地观察和修改数组的值。<br />
<img src="https://s2.ax1x.com/2020/02/10/14UyJP.png" alt="" /><br />
如今的spyder系也在壮大发展，发展出了很多第三方插件如Spyder Notebook，Spyder Terminal，Spyder UnitTest，Spyder Reports等（听名字应该就知道是干啥的了😂），下面这个图就是Spyder Reports了，很像markdown是不是。<br />
<img src="https://s2.ax1x.com/2020/02/10/14U7WV.png" alt="" /></p>
<h3 id="机器学习与深度学习"><a class="markdownIt-Anchor" href="#机器学习与深度学习"></a> 机器学习与深度学习</h3>
<p>Anaconda内置了机器学习库sklearn；深度学习框架：Tensorflow(这个没记错的话还是要后面再手动安装)和Theano，当然我选择pytorch。😋</p>
<h3 id="优秀的数据分析库"><a class="markdownIt-Anchor" href="#优秀的数据分析库"></a> 优秀的数据分析库</h3>
<p>Anaconda内置的数据分析库有Dask，numpy，pandas和Numba等。<br />
Dask是一款用于分析计算的灵活并行计算库。由两部分组成：</p>
<ul>
<li>针对计算优化的动态任务调度。这与Airflow，Luigi，Celery或Make类似，但针对交互式计算工作负载进行了优化。</li>
<li>“大数据”集合， 像并行数组，数据框和列表一样，它们将通用接口（如NumPy，Pandas或Python迭代器）扩展到大于内存或分布式环境。 这些并行集合运行在动态任务调度器之上。</li>
</ul>
<p>Dask属于开源项目，支持和numpy或pandas共同使用，扩展他们的工作流，支持时间序列、商业智能和大数据通用数据的应用。等于是更加专业化。</p>
<p>Numpy和pandas可能见得多了，前者是使用Python进行科学计算的基础软件包。除其他外，它包括：</p>
<ul>
<li>功能强大的N维数组对象。</li>
<li>精密广播功能函数。</li>
<li>集成 C/C+和Fortran 代码的工具。</li>
<li>强大的线性代数、傅立叶变换和随机数功能。</li>
</ul>
<p><strong>pandas</strong>🐼是基于NumPy 的一种工具，该工具是为了解决数据分析任务而创建的。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的工具。pandas提供了大量能使我们快速便捷地处理数据的函数和方法。你很快就会发现，它是使Python成为强大而高效的数据分析环境的重要因素之一。如果说numpy更倾向于数值计算，pandas则更倾向于数据分析。<br />
<img src="https://s2.ax1x.com/2020/02/10/14dX5R.png" alt="" /><br />
Numba：一个来自Anaconda的Python编译器，可以编译Python代码，以便在支持CUDA的GPU或多核CPU上执行。Numba专为面向阵列的计算任务而设计，就像广泛使用的NumPy库一样。面向阵列的计算任务中的数据并行性非常适合GPU等加速器。 Numba了解NumPy数组类型，并使用它们生成有效的编译代码，以便在GPU或多核CPU上执行。</p>
<h3 id="数据可视化"><a class="markdownIt-Anchor" href="#数据可视化"></a> 数据可视化</h3>
<p>数据可视化一直是python的重要组成部分，Python中有很多优秀的可视化工具，大致可以分为三类：</p>
<ul>
<li>基于 matplotlib 的可视化库(比如seaborn等)</li>
<li>基于JS的可视化库(比如bokeh，plotly等)</li>
<li>matplotlib和JS结合的可视化库</li>
</ul>
<p><strong>matplotlib</strong>是Python中一个低级可视化工具，其提供了两种绘图方式。其中一种类似MATLAB，通过调用函数直接绘图。函数式绘图使得使用matplotlib绘图更加方便，而且产生的图达到了出版质量，但是同样也存在一些缺点：</p>
<ul>
<li>调用细节被掩盖，不便于理解matpltolib的底层操作</li>
<li>绘图处理速度低，尤其是在实时交互和图形快速更新等方面<br />
<img src="https://s2.ax1x.com/2020/02/10/14w1aj.jpg" alt="" /></li>
</ul>
<p><strong>Seaborn</strong>基于matplotlib，提供了一些更美观的配置选项，而且封装了很多统计绘图函数。所以如果你在做数据分析时，需要进行一些统计分析和可视化，Seaborn则是比较好的选择。<br />
<img src="https://s2.ax1x.com/2020/02/10/14wJGq.jpg" alt="" /></p>
<p>除了基于matplotlib的可视化库之外，Python中有很多基于JS的可视化库，其中比较流行的是：Bokeh，HoloViews，Plotly等。基于JS开发的可视化库相对于matpltolib来说，其优势之一是：<strong>交互可视化。</strong></p>
<p>HoloViews是PyViz的产品之一，此外还有一些其他产品，比如GeoViews，专门用来处理<strong>地理学数据的交互式可视化。</strong><br />
<img src="https://s2.ax1x.com/2020/02/10/14w6Rx.jpg" alt="HoloViews" /></p>
<p>涉及到地理空间可视化时，可以使用<strong>gmaps</strong>将数据可视化到google地图并嵌入到jupyter notebook中。</p>
<h3 id="更多更多"><a class="markdownIt-Anchor" href="#更多更多"></a> 更多更多。。。</h3>
<p>打开你的conda，输入conda list，会看到全部anaconda支持的包，如支持写入Excel的xlwt，应用于网络学科的networkx等等等等。</p>

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            <p>原文作者：<a href="http://xiuzhedorothy.gitee.io">宇航猫休蛰</a>
            <p>原文链接：<a href="http://xiuzhedorothy.gitee.io/2020/02/10/chong-xin-ren-shi-anaconda/">http://xiuzhedorothy.gitee.io/2020/02/10/chong-xin-ren-shi-anaconda/</a>
            <p>发表日期：<a href="http://xiuzhedorothy.gitee.io/2020/02/10/chong-xin-ren-shi-anaconda/">February 10th 2020, 10:31:40 am</a>
            <p>更新日期：<a href="http://xiuzhedorothy.gitee.io/2020/02/10/chong-xin-ren-shi-anaconda/">March 1st 2020, 9:21:09 pm</a>
            <p>版权声明：本文采用<a rel="license noopener" href="http://creativecommons.org/licenses/by-nc/4.0/" target="_blank">知识共享署名-非商业性使用 4.0 国际许可协议</a>进行许可</p>
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        <ol class="toc"><li class="toc-item toc-level-2"><a class="toc-link" href="#what-is-anaconda"><span class="toc-number">1.</span> <span class="toc-text"> What is Anaconda ?</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#anaconda内置的那些库"><span class="toc-number">2.</span> <span class="toc-text"> Anaconda内置的那些库</span></a><ol class="toc-child"><li class="toc-item toc-level-3"><a class="toc-link" href="#jupyter系"><span class="toc-number">2.1.</span> <span class="toc-text"> jupyter系</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#spyder"><span class="toc-number">2.2.</span> <span class="toc-text"> spyder</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#机器学习与深度学习"><span class="toc-number">2.3.</span> <span class="toc-text"> 机器学习与深度学习</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#优秀的数据分析库"><span class="toc-number">2.4.</span> <span class="toc-text"> 优秀的数据分析库</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#数据可视化"><span class="toc-number">2.5.</span> <span class="toc-text"> 数据可视化</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#更多更多"><span class="toc-number">2.6.</span> <span class="toc-text"> 更多更多。。。</span></a></li></ol></li></ol>
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    缺失模块。<br/>
    1、请确保node版本大于6.2<br/>
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jsonContent:
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    tags: true</pre>
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